Double Bayesian Predictive Stacking for (univariate) Spatial Analysis - Tutotial

We provide a brief tutorial of the spBPS package. Here we shows the implementation of the Double Bayesian Predictive Stacking on synthetically univariate generated data. In particular, we focus on parallel computing using the packages parallel, doParallel; but it is not mandatory: it suffices to make it sequential. For any further details please refer to (Presicce and Banerjee 2024). More examples, for multivariate data, are available in documentation, and functions help.

library(spBPS)

Working packages

library(foreach)
library(parallel)
library(doParallel)
library(tictoc)
library(MBA)
library(classInt)
library(RColorBrewer)
library(sp)
library(fields)
library(mvnfast)

Data generation

We generate data from the model detailed in Equation (2.4) (Presicce and Banerjee 2024), over a unit square.

# dimensions
n <- 1000
u <- 250
p <- 2

# parameters
B <- c(-0.75, 1.85)
tau2 <- 0.25
sigma2 <- 1
delta <- tau2/sigma2
phi <- 4

set.seed(4-8-15-16-23-42)
# generate sintethic data
crd <- matrix(runif((n+u) * 2), ncol = 2)
X_or <- cbind(rep(1, n+u), matrix(runif((p-1)*(n+u)), ncol = (p-1)))
D <- arma_dist(crd)
Rphi <- exp(-phi * D)
W_or <- matrix(0, n+u) + mniw::rmNorm(1, rep(0, n+u), sigma2*Rphi)
Y_or <- X_or %*% B + W_or + mniw::rmNorm(1, rep(0, n+u), diag(delta*sigma2, n+u))

# train data
crd_s <- crd[1:n, ]
X <- X_or[1:n, ]
W <- W_or[1:n, ]
Y <- Y_or[1:n, ]

# prediction data
crd_u <- crd[-(1:n), ]
X_u <- X_or[-(1:n), ]
W_u <- W_or[-(1:n), ]
Y_u <- Y_or[-(1:n), ]

Subset posterior models

We opt to divide the original data into K=2, such that each subsets results in 500 locations.

# hyperparameters values
delta_seq <- c(0.2, 0.25, 0.3)
phi_seq <- c(3, 4, 5)

# function for the fit loop
fit_loop <- function(i) {

  Yi <- data_part$Y_list[[i]]
  Xi <- data_part$X_list[[i]]
  crd_i <- data_part$crd_list[[i]]
  p <- ncol(Xi)
  bps <- spBPS::BPS_weights(data = list(Y = Yi, X = Xi),
                           priors = list(mu_b = matrix(rep(0, p)),
                                         V_b = diag(10, p),
                                         a = 2,
                                         b = 2), coords = crd_i,
                           hyperpar = list(delta = delta_seq, phi = phi_seq), K = 5)
  w_hat <- bps$W
  epd <- bps$epd

  result <- list(epd, w_hat)
  return(result)

}

# function for the pred loop
pred_loop <- function(r) {

  ind_s <- subset_ind[r]
  Ys <- matrix(data_part$Y_list[[ind_s]])
  Xs <- data_part$X_list[[ind_s]]
  crds <- data_part$crd_list[[ind_s]]
  Ws <- W_list[[ind_s]]
  result <- spBPS::BPS_post(data = list(Y = Ys, X = Xs), coords = crds,
                           X_u = X_u, crd_u = crd_u,
                           priors = list(mu_b = matrix(rep(0, p)),
                                         V_b = diag(10, p),
                                         a = 2,
                                         b = 2),
                           hyperpar = list(delta = delta_seq, phi = phi_seq),
                           W = Ws, R = 1)

  return(result)
}


# subsetting data
subset_size <- 500
K <- n/subset_size
data_part <- subset_data(data = list(Y = matrix(Y), X = X, crd = crd_s), K = K)

Double BPS parallel fit

Parallel implementation, exploiting 2 computing core.

# number of clusters for parallel implementation
n.core <- 2

# list of function
funs_fit <- lsf.str()[which(lsf.str() != "fit_loop")]

# list of function
funs_pred <- lsf.str()[which(lsf.str() != "pred_loop")]

# starting cluster
cl <- makeCluster(n.core)
registerDoParallel(cl)

# timing
tic("total")

# parallelized subset computation of GP in different cores
tic("fit")
obj_fit <- foreach(i = 1:K, .noexport = funs_fit) %dopar% { fit_loop(i) }
fit_time <- toc()

gc(verbose = F)
# Combination using double BPS
tic("comb")
comb_bps <- BPS_combine(obj_fit, K, 1)
comb_time <- toc()
Wbps <- comb_bps$W
W_list <- comb_bps$W_list

gc(verbose = F)
# parallelized subset computation of GP in different cores
R <- 250
subset_ind <- sample(1:K, R, T, Wbps)
tic("prediction")
predictions <- foreach(r = 1:R, .noexport = funs_pred) %dopar% { pred_loop(r) }
prd_time <- toc()

# timing
tot_time <- toc()

# closing cluster
stopCluster(cl)
gc(verbose = F)

Results collection

# statistics computations W
pred_mat_W <- sapply(1:R, function(r){predictions[[r]][[1]]})
post_mean_W <- rowMeans(pred_mat_W)
post_var_W <- apply(pred_mat_W, 1, sd)
post_qnt_W <- apply(pred_mat_W, 1, quantile, c(0.025, 0.975))

# Empirical coverage for W
coverage_W <- mean(W_u >= post_qnt_W[1,] & W_u <= post_qnt_W[2,])
cat("Empirical coverage for Spatial process:", round(coverage_W, 3),"\n")
#> Empirical coverage for Spatial process: 0.972

# statistics computations Y
pred_mat_Y <- sapply(1:R, function(r){predictions[[r]][[2]]})
post_mean_Y <- rowMeans(pred_mat_Y)
post_var_Y <- apply(pred_mat_Y, 1, sd)
post_qnt_Y <- apply(pred_mat_Y, 1, quantile, c(0.025, 0.975))

# Empirical coverage for Y
coverage_Y <- mean(Y_u >= post_qnt_Y[1,] & Y_u <= post_qnt_Y[2,])
cat("Empirical coverage for Response:", round(coverage_Y, 3),"\n")
#> Empirical coverage for Response: 0.96

# Root Mean Square Prediction Error
rmspe_W <- sqrt( mean( (W_u - post_mean_W)^2 ) )
rmspe_Y <- sqrt( mean( (Y_u - post_mean_Y)^2 ) )
cat("RMSPE for Spatial process:", round(rmspe_W, 3), "\n")
#> RMSPE for Spatial process: 0.472
cat("RMSPE for Response:", round(rmspe_Y, 3), "\n")
#> RMSPE for Response: 0.599

Plot results

Presicce, Luca, and Sudipto Banerjee. 2024. “Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach.” arXiv Preprint, arXiv:2410.09504. https://doi.org/10.48550/arXiv.2410.09504.